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Technical Brief

Robot Path Planning in Uncertain Environments: A Language-Measure-Theoretic Approach

[+] Author and Article Information
Devesh K. Jha

Mechanical & Nuclear Engineering Department
Pennsylvania State University,
University Park, PA 16802
e-mail: dkj5042@psu.edu

Yue Li

Mechanical & Nuclear Engineering Department
Pennsylvania State University,
University Park, PA 16802
e-mail: yol5214@psu.edu

Thomas A. Wettergren

Naval Undersea Warfare Center,
Newport, RI 02841;
Mechanical & Nuclear Engineering Department
Pennsylvania State University,
University Park, PA 16802
e-mail: t.a.wettergren@ieee.org

Asok Ray

Fellow ASME
Mechanical & Nuclear Engineering Department
Pennsylvania State University,
University Park, PA 16802
e-mail: axr2@psu.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 23, 2014; final manuscript received May 31, 2014; published online October 21, 2014. Assoc. Editor: Jongeun Choi. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Dyn. Sys., Meas., Control 137(3), 034501 (Oct 21, 2014) (7 pages) Paper No: DS-14-1028; doi: 10.1115/1.4027876 History: Received January 23, 2014; Revised May 31, 2014

This paper addresses the problem of goal-directed robot path planning in the presence of uncertainties that are induced by bounded environmental disturbances and actuation errors. The offline infinite-horizon optimal plan is locally updated by online finite-horizon adaptive replanning upon observation of unexpected events (e.g., detection of unanticipated obstacles). The underlying theory is developed as an extension of a grid-based path planning algorithm, called ν, which was formulated in the framework of probabilistic finite state automata (PFSA) and language measure from a control-theoretic perspective. The proposed concept has been validated on a simulation test bed that is constructed upon a model of typical autonomous underwater vehicles (AUVs) in the presence of uncertainties.

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Figures

Grahic Jump Location
Fig. 3

Real-time replanning over a finite time horizon T

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Fig. 4

Replanning with |T|=2 for different characteristic weights

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Fig. 1

Illustration of the effects of uncontrollable transitions due to the disturbances. It is shown how uncontrollable transitions may cause the robot to end up in a different neighboring state of the origin cell, thus interfering with control actions.

Grahic Jump Location
Fig. 2

Optimal paths under different directions of the ocean current. (The vehicle is constrained to stay in the box and not hit the walls.)

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